



'''simple docstring'''






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCAmelCase:   Union[str, Any]					=		logging.get_logger(__name__)

lowerCAmelCase:   Tuple					=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase:   Dict					=		{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase:   Optional[int]					=		{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase:   int					=		{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase:   Union[str, Any]					=		{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
lowerCAmelCase:   Any					=		{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
lowerCAmelCase:   Optional[int]					=		{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


lowerCAmelCase:   Optional[Any]					=		{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase:   int					=		{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase:   Union[str, Any]					=		{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__					=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    lowercase__					=       DPRContextEncoderTokenizer

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__					=       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    lowercase__					=       DPRQuestionEncoderTokenizer
lowerCAmelCase:   Optional[int]					=		collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowerCAmelCase:   Any					=		collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase:   Optional[int]					=		r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(lowerCamelCase__      )
class 				a__:







    def __call__( self					:    Dict		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Optional[str] = None		,     __snake_case					:    Optional[str] = None		,     __snake_case					:    Union[bool, str] = False		,     __snake_case					:    Union[bool, str] = False		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     __snake_case					:    Optional[bool] = None		,     **__snake_case					:    Dict		,     ):
      if titles is None and texts is None:
        return super().__call__(
            __snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     return_tensors=__snake_case		,     return_attention_mask=__snake_case		,     **__snake_case		,     )
      elif titles is None or texts is None:
        a		:   str	  =	titles if texts is None else texts
        return super().__call__(
            __snake_case		,     __snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     return_tensors=__snake_case		,     return_attention_mask=__snake_case		,     **__snake_case		,     )
      a		:   Optional[int]	  =	titles if not isinstance(__snake_case		,     __snake_case      ) else [titles]
      a		:   Tuple	  =	texts if not isinstance(__snake_case		,     __snake_case      ) else [texts]
      a		:   str	  =	len(__snake_case      )
      a		:   Union[str, Any]	  =	questions if not isinstance(__snake_case		,     __snake_case      ) else [questions] * n_passages
      assert len(__snake_case      ) == len(
          __snake_case      ), F"""There should be as many titles than texts but got {len(__snake_case      )} titles and {len(__snake_case      )} texts."""
      a		:   Dict	  =	super().__call__(__snake_case		,     __snake_case		,     padding=__snake_case		,     truncation=__snake_case      )['input_ids']
      a		:   Any	  =	super().__call__(__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case      )['input_ids']
      a		:   Union[str, Any]	  =	{
          'input_ids': [
              (encoded_question_and_title + encoded_text)[:max_length]
              if max_length is not None and truncation
              else encoded_question_and_title + encoded_text
              for encoded_question_and_title, encoded_text in zip(__snake_case		,     __snake_case      )
          ]
      }
      if return_attention_mask is not False:
        a		:   Any	  =	[]
        for input_ids in encoded_inputs["input_ids"]:
          attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
        a		:   Union[str, Any]	  =	attention_mask
      return self.pad(__snake_case		,     padding=__snake_case		,     max_length=__snake_case		,     return_tensors=__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    BatchEncoding		,     __snake_case					:    DPRReaderOutput		,     __snake_case					:    int = 16		,     __snake_case					:    int = 64		,     __snake_case					:    int = 4		,     ):
      a		:   Optional[int]	  =	reader_input['input_ids']
      a  ,      a  ,      a		:   int	  =	reader_output[:3]
      a		:   int	  =	len(__snake_case      )
      a		:   Dict	  =	sorted(range(__snake_case      )		,     reverse=__snake_case		,     key=relevance_logits.__getitem__      )
      a		:   List[DPRReaderOutput]	  =	[]
      for doc_id in sorted_docs:
        a		:   List[Any]	  =	list(input_ids[doc_id]      )
        # assuming question & title information is at the beginning of the sequence
        a		:   str	  =	sequence_ids.index(self.sep_token_id		,     2      ) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
          a		:   Any	  =	sequence_ids.index(self.pad_token_id      )
        else:
          a		:   Any	  =	len(__snake_case      )

        a		:   List[str]	  =	self._get_best_spans(
            start_logits=start_logits[doc_id][passage_offset:sequence_len]		,     end_logits=end_logits[doc_id][passage_offset:sequence_len]		,     max_answer_length=__snake_case		,     top_spans=__snake_case		,     )
        for start_index, end_index in best_spans:
          start_index += passage_offset
          end_index += passage_offset
          nbest_spans_predictions.append(
              DPRSpanPrediction(
                  span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]		,     relevance_score=relevance_logits[doc_id]		,     doc_id=__snake_case		,     start_index=__snake_case		,     end_index=__snake_case		,     text=self.decode(sequence_ids[start_index : end_index + 1]      )		,     )      )
        if len(__snake_case      ) >= num_spans:
          break
      return nbest_spans_predictions[:num_spans]







    def      lowercase_			( self					:    Tuple		,     __snake_case					:    List[int]		,     __snake_case					:    List[int]		,     __snake_case					:    int		,     __snake_case					:    int		,     ):
      a		:   Tuple	  =	[]
      for start_index, start_score in enumerate(__snake_case      ):
        for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]      ):
          scores.append(((start_index, start_index + answer_length), start_score + end_score)      )
      a		:   Tuple	  =	sorted(__snake_case		,     key=lambda __snake_case      : x[1]		,     reverse=__snake_case      )
      a		:   int	  =	[]
      for (start_index, end_index), score in scores:
        assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
        a		:   Any	  =	end_index - start_index + 1
        assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
        if any(
            start_index <= prev_start_index <= prev_end_index <= end_index
            or prev_start_index <= start_index <= end_index <= prev_end_index
            for (prev_start_index, prev_end_index) in chosen_span_intervals      ):
          continue
        chosen_span_intervals.append((start_index, end_index)      )

        if len(__snake_case      ) == top_spans:
          break
      return chosen_span_intervals

@add_end_docstrings(lowerCamelCase__      )
class 				a__(   lowerCamelCase__						,					lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       READER_PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__					=       READER_PRETRAINED_INIT_CONFIGURATION
    lowercase__					=       ["""input_ids""", """attention_mask"""]
    lowercase__					=       DPRReaderTokenizer




'''simple docstring'''






import os

try:
     from .build_directory_md import good_file_paths
except ImportError:
     from build_directory_md import good_file_paths  # type: ignore

lowerCAmelCase:   Optional[int]					=		list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowerCAmelCase:   Optional[Any]					=		[file for file in filepaths if file != file.lower()]
if upper_files:
     print(F"{len(upper_files)} files contain uppercase characters:")
     print('\n'.join(upper_files) + '\n')

lowerCAmelCase:   int					=		[file for file in filepaths if ' ' in file]
if space_files:
     print(F"{len(space_files)} files contain space characters:")
     print('\n'.join(space_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if '-' in file]
if hyphen_files:
     print(F"{len(hyphen_files)} files contain hyphen characters:")
     print('\n'.join(hyphen_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if os.sep not in file]
if nodir_files:
     print(F"{len(nodir_files)} files are not in a directory:")
     print('\n'.join(nodir_files) + '\n')

lowerCAmelCase:   int					=		len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
     import sys

     sys.exit(bad_files)